Análisis de la situación con 30% de decremento en ventas

An enhanced method (Method 2+RF) was therefore developed based upon a method of discarding UE location reports where the reported location did not match with the associated RF measurements. An RF fingerprint table was constructed for each location within the simulation area using coverage plots produced by the Network Simulator. It was assumed that the construction of such a table would be possible in a reality using a predicted multi- cell 3D RSCP fingerprint matrix generated from a commercial RF planning tool combined

over time with UE measurement reports known to have very accurate location information to correct any discrepancies between the planned and actual coverage arrays, similar to that proposed in [94]. The 3D RSCP fingerprint matrix generated by the Network Simulator contained an RF fingerprint for each 5x5m bin, an example of such a fingerprint generated by the simulation tool is given below in Table 5.7.

Table 5.7 Example RF Fingerprint Entry in the 3D RSCP Fingerprint Matrix.

Serving Cell Cell Scrambling Code Cell RSCP [dBm]

1st 49 -85.52

2nd 47 -94.51

3rd 33 -95.80

4th 34 -95.97

5th 58 -98.65

In order to discard erroneous user locations from the user distribution matrix used as input into the 2D circular filter process, each UE measurement report was given a location

reliability rank based upon the comparison of its reported location and best serving cells

against the 3D RSCP fingerprint matrix as follows:

If the UE measurement report’s best serving cell matched the best serving cell stored in the 3D RSCP fingerprint matrix for the location reported in the UE measurement report then the UE measurement was given a location reliability rank of 1.

If the UE measurement report’s 1st _{and 2}nd _{best cells matched the 1}st _{and 2}nd _best

cells stored in the 3D RSCP fingerprint matrix for the location reported in the UE measurement report then the UE measurement was given a location reliability rank of 2.

If the UE measurement report’s 1st_{, 2}nd _{and 3}rd _{best cells matched the 1}st_{, 2}nd _{and 3}rd

best cells stored in the 3D RSCP fingerprint matrix for the location reported in the UE measurement report then the UE measurement was given a location reliability

rank of 3.

If the UE measurement report’s 1st_{, 2}nd_{, 3}rd _{and 4}th _{best cells matched the 1}st_{, 2}nd_{, 3}rd

and 4th _{best cells stored in the 3D RSCP fingerprint matrix for the location reported}

in the UE measurement report then the UE measurement was given a location

If the UE measurement report’s 1st_{, 2}nd_{, 3}rd_{, 4}th _{and 5}th _{best cells matched the 1}st_{, 2}nd_,

3rd_{, 4}th _{and 5}th _{best cells stored in the 3D RSCP fingerprint matrix for the location}

reported in the UE measurement report then the UE measurement was given a

location reliability rank of 5.

It should be noted that only the order of the cells and not the actual RSCP levels were used in this case to perform the fingerprint look up and location report ranking. The reason for this was that it was felt that because of shadow fading and inaccuracies of the RSCP values actually reported by the UE (3GPP TS 25.133 [96] specifies a +/- 11dB absolute accuracy requirement and a +/- 3dB relative requirement) then the RF fingerprinting method proposed here was more likely to be a practical reality if it only considered the relate rankings rather than absolute RSCP levels. That is not to say that methods based on both cell ranking and absolute or relative RSCP levels are not possible and this is definitely an area worthy of further investigation.

Having given each UE location report a location reliability rank, it was then possible to build up user distribution Xmaps using only measurement reports with particular location reliability rankings. For example shown in Figures 5.16 to 5.19 are examples of user distributions Xmaps constructed from the same reported UE locations but with different location reliability rankings used to filter the users used to create the users distribution Xmap. As can be seen clearly from these examples the higher the location reliability ranking threshold applied to the reports the much greater the location accuracy becomes of the retained samples.

Figure 5.16 Left-hand side: Original Hotspot location with reported UE locations. Right-

Figure 5.17 Left-hand side: Only UE reports with RF fingerprint reliability rank ≥ 1

retained. Right-hand side: Only UE reports with RF fingerprint reliability rank ≥ 2 retained. (RMS location error = 200m).

Figure 5.18 Left-hand side: Only UE reports with RF fingerprint reliability rank ≥ 3

retained. Right-hand side: Only UE reports with RF fingerprint reliability rank ≥ 4 retained. (RMS location error = 200m).

Figure 5.19 Reported UE locations (RMS location error = 200m), only UE reports with RF

5.6.2 Placement Results using Method 2+RF: Combining 2D Circular

Filtering with RF Fingerprinting

As stated previously the aim of applying the RF fingerprinting technique to the user locations was to provide the 2D circular filter with much more reliable UE locations in order for it to better estimate the location of the traffic hotspots. Shown in Figures 5.20 and 5.21 are the output from the 2D circular filter process before and after applying the RF fingerprint location reliability ranking prior to application of the filter. As can be see applying the user location reliability ranking prior to the 2D circular filtering process certainly produces better defined peaks in the output of the filtering process and as will be shown allows the lamppost placement algorithm to place lampposts much closer to the traffic hotspots than was the case without RF fingerprinting being applied.

Figure 5.20 Original hotspot locations and circular sliding filter results without RF

fingerprinting applied (RMS location error = 200m).

Figure 5.21 Circular sliding filter results after RF fingerprinting applied (RMS location

error = 200m). Reliability rank ≥ 1 applied (left-hand figure) and reliability rank = 5 applied (right-hand figure).

Users Captured

Further Monte Carlo simulation runs were performed with placement Method 2+RF in order to evaluate whether the RF fingerprinting provided significant gains over the previous Methods 1 & 2. Shown in Figures 5.22 and 5.23 are the detailed results for the 100m cell radius case for RMS location errors of 150m and 200m . As can be seen from the figures the RF fingerprinting technique definitely improves the ability of the placement algorithm to estimate the location of the hotspots especially for an RMS location error of 200m. Also it can be seen that only considering the UE reports with a reliability rank = 5 provides the greatest gains in terms of the users captured per post by the placement algorithm.

Figure 5.22 Method 2+RF results. Left-hand figure: CDF of users per Lamppost. Right-

hand figure: CDF of distance from lamppost to target hotspot. (Cell radius = 100m, RMS location error = 200m).

Figure 5.23 Method 2+RF results. Left-hand figure: CDF of users per lamppost. Right-

hand figure: CDF of distance from lamppost to target hotspot. (Cell radius = 100m, RMS location error = 150m).

The overall results for Method 2+RF applied to all three small cell radii considered are given below in Table 5.8 and represented graphically in Figure 5.24. Starting with the 200m cell radius case, as seen for both Methods 1, 2 and 2+RF cells of this size tend to capture the majority of traffic because of their size compared to the inaccuracy of the hotspot location estimation. Although Method 2+RF does provide a gain over Method 1 at the largest RMS

location error of 200m, it does not provide any gain over Method 2, and therefore it is concluded that no further gains are seen when applying RF fingerprinting compared to Method 2 in the case of the placement of 200m radius small cells.

However for the cases of 50 and 100m radii small cell placement, whilst it is seen that for low RMS location error values Method 2+RF provides little benefit over that of Method 1 for larger RMS location errors of 100m or greater significant captured traffic gains as high as 139% are possible especially for the largest RMS location error of 200m.

Table 5.8 Method 2+RF (Reliability Rank=5) overall results showing traffic gains over

Method 1. RMS Location Error 0m 50m 100m 150m 200m Microcell Radius [m] Users Served % Gain over Meth. 1 Users Served % Gain over Meth. 1 Users Served % Gain over Meth. 1 Users Served % Gain over Meth. 1 Users Served % Gain over Meth. 1 50 15295 2% 13941 3% 10916 21% 8187 57% 7237 139% 100 24457 2% 23937 0% 22852 2% 21288 24% 20209 56% 200 24951 1% 24797 1% 24716 0% 24533 4% 23826 10%

Figure 5.24 Method 2+RF (Reliability Rank=5) overall results compared against those of

placement Methods 1 & 2.

0 5000 10000 15000 20000 25000 30000 0 25 50 75 100 125 150 175 200 Us e rs c ap tu re d Geo-location RMS Error [m]

Average Traffic Captured vs. Geo-location Accuracy

50m Cell Radius (Method 1) 50m Cell Radius (Method 2) 50m Cell Radius (Method 2+RF) 100m Cell Radius (Method 1) 100m Cell Radius (Method 2) 100m Cell Radius (Method 2+RF) 200m Cell Radius (Method 1) 200m Cell Radius (Method 2) 200m Cell Radius (Method 2+RF)

5.6.3 Small Cell Placement Enhancement Through RF Fingerprinting